Abstract Graph Neural Networks (GNN) are an emerging field for learning on non-Euclidean
data. Recently, there has been increased interest in designing GNN that scales to large …

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs.
For a pair of nodes and, the PPR value equals the probability that an-discounted random …

Efficient computation of node proximity queries such as transition probabilities, Personalized
PageRank, and Katz are of fundamental importance in various graph mining and learning …

Entity-oriented search tasks heavily rely on exploiting unstructured and structured
collections. Moreover, it is frequent for text corpora and knowledge bases to provide …

Node embedding learns a low-dimensional representation for each node in the graph.
Recent progress on node embedding shows that proximity matrix factorization methods gain …

PageRank is a famous measure of graph centrality that has numerous applications in
practice. The problem of computing a single node's PageRank has been the subject of …

Size-constrained community search, retrieving a size-bounded high-quality subgraph
containing user-specified query vertices, has been extensively studied in graph analysis …

Temporal community search is an important task in graph analysis, which has been widely
used in many practical applications. However, existing methods suffer from two major …

Conductance-based graph clustering has been recognized as a fundamental operator in
numerous graph analysis applications. Despite the significant success of conductance …

Given a graph G where each node is associated with a set of attributes, and a parameter k
specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nodes …